On-Line Monitoring of Enzymatic Degumming of Soybean Oil Using Near-Infrared Spectroscopy.

Degumming is an oil refinement process in which the naturally occurring phospholipids in crude vegetable oils are removed. Enzymatic degumming results in higher oil yield and more cost-efficient processing compared to traditional degumming processes using only water or acid. Phospholipase C hydrolyses phospholipids into diglycerides and phosphate groups during degumming. The diglyceride content can therefore be considered a good indicator of the state of the enzymatic reaction. This study investigates the use of near-infrared (NIR) spectroscopy and chemometrics to monitor the degumming process by quantifying diglycerides in soybean oil in both off-line and on-line settings. Fifteen enzymatic degumming lab scale batches originating from a definitive screening design (with varying water, acid, and enzyme dosages) were investigated with the aim to develop a NIR spectroscopy prediction method. By applying tailored preprocessing and variable selection methods, the diglyceride content can be predicted with a root mean square error of prediction of 0.06% (w/w) for the off-line set-up and 0.07% (w/w) for the on-line set-up. The results show that the diglyceride content is a good indicator of the enzyme performance and that NIR spectroscopy is a suitable analytical technique for robust real-time diglyceride quantification.

Multivariate approach for the authentication of vanilla using infrared and Raman spectroscopy.

Many different versions of vanilla extracts exist in the market in a variety of origins, purity levels and composition with little effective regulation. In this study, vanilla is authenticated both in terms of purity and geographical origin applying a multivariate approach using near infrared (NIR), mid infrared (MIR) and Raman spectroscopy following a complex experimental design. Partial least squares-discriminant analysis (PLS-DA) was applied to the spectral data to produce qualitative models. The prediction accuracy of the models was externally validated from the specific success/error contingencies. The results showed that MIR and Raman are reliable for authenticating vanilla in terms of purity, obtaining sensitivity, specificity, precision, and efficiency values equal to 1.00, and Raman is especially suitable for indicating the geographical origin of vanilla extracts, achieving performance metrics around 0.9.

Prediction of α-Lactalbumin and ß-Lactoglobulin Composition of Aqueous Whey Solutions Using Fourier Transform Mid-Infrared Spectroscopy and Near-Infrared Spectroscopy.

Characterization and quantification of individual whey proteins are of crucial importance to many industrial dairy processes. Labor intensive wet-chemical methods are still being used for this purpose, but a rapid quantification method for individual whey proteins is highly desired. This work investigate the utility of Fourier transform mid-infrared spectroscopy and Fourier transform near-infrared spectroscopy for rapid quantification of the two main whey proteins (ß-lactoglobulin and α-lactalbumin) in complex aqueous whey solutions simulating production process streams. MIR and NIR spectra obtained on whey samples with known and varying amounts of the proteins of interest and are used to develop partial least squares prediction models. Selection of informative wavelength regions allowed for prediction of ß-lactoglobulin and α-lactalbumin concentrations with very high precision and accuracy (root mean square error of cross-validation, or RMSECV, of 0.6% and R2 of 0.99 for NIR), demonstrating the potential of being implemented for rapid in-line monitoring of protein composition in industrial whey streams. Two-dimensional MIR-NIR correlation spectroscopy is used to identify the most informative parts of the NIR spectra in relation to protein secondary structure. In addition multivariate curve resolution is applied to the MIR data to resolve mixture spectra and to elucidate the spectral ranges that were most useful in distinguishing between the two whey proteins. The study concludes that NIR spectroscopy has potential for accurate in-line protein quantification and overall secondary protein structure quantification which open new possibilities for in-line industrial applications.